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1. Quantum algorithms for attacking hardness assumptions in classical and post‐quantum cryptography

   https://doi.org/10.1049/ise2.12081  

   Biasse, J‐F; Bonnetain, X.; Kirshanova, E.; Schrottenloher, A.; Song, F. (August 2022, IET Information Security)

   Abstract In this survey, the authors review the main quantum algorithms for solving the computational problems that serve as hardness assumptions for cryptosystem. To this end, the authors consider both the currently most widely used classically secure cryptosystems, and the most promising candidates for post‐quantum secure cryptosystems. The authors provide details on the cost of the quantum algorithms presented in this survey. The authors furthermore discuss ongoing research directions that can impact quantum cryptanalysis in the future. 

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2. A lower bound on the mean value of the Erdős–Hooley Delta function

   https://doi.org/10.1112/plms.12618  

   Ford, Kevin; Koukoulopoulos, Dimitris; Tao, Terence (July 2024, Proceedings of the London Mathematical Society)

   Abstract We give an improved lower bound for the average of the Erdős–Hooley function , namely for all and any fixed , where is an exponent previously appearing in work of Green and the first two authors. This improves on a previous lower bound of of Hall and Tenenbaum, and can be compared to the recent upper bound of of the second and third authors. 

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3. Learning designs that empower: navigating sandbox data science at the intersection of computing, big data and social media

   https://doi.org/10.1108/ILS-12-2023-0211  

   Barany, Amanda; Scarola, Andi Danielle; Acquah, Alex; Reza, Sayed Mohsin; Johnson, Michael A; Walker, Justice (August 2024, Information and Learning Sciences)

   PurposeThere is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how “sandbox” or open-inquiry data science with social media supports learning. Design/methodology/approachThis paper offers vignettes from an illustrative youth study case that highlights the pedagogical prospects and obstacles tied to designing for open-ended inquiry with computational data science to access or “scrape” Twitter/X. The youth case showcases how social media can be taken up productively and in ways that facilitate epistemological agency, an approach where individuals actively shape understanding and knowledge-creation processes, highlighting the potentially transformative impact this approach might have in empowering learners to engage productively. FindingsThe authors identify three key affordances for learning that emerged from the illustrative case: (1) flexible opportunities for content-specific domain mastery, (2) situated inquiry that embodies next-generation science practices and (3) embedded computational skill development. The authors discuss these findings in relation to contemporary education needs to broaden participation in data science and computing. Originality/valueTo address challenges in current data science education associated with supporting sustained and productive engagement in computing-based data science, the authors leverage a “sandbox” approach – an original pedagogical framework to support open inquiry with precollege groups. The authors demonstrate how “big data” drawn from social media with high school-aged youth supports learning designs and outcomes by emphasizing learner interests and authentic practice. 

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4. Using network visualizations to engage elementary students in locally relevant data literacy

   https://doi.org/10.1108/ILS-06-2023-0069  

   Zhou, Mengxi; Steinberg, Selena; Stiso, Christina; Danish, Joshua A.; Craig, Kalani (December 2023, Information and Learning Sciences)

   PurposeThis study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data. Design/methodology/approachThe researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets. FindingsPre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them. Originality/valueTo the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum. 

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5. Dynamic distribution modelling of the swamp tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20‐year bushfire regime

   https://doi.org/10.1111/een.13216  

   Goodman, Aaron_M; Kass, Jamie_M; Ware, Jessica (December 2022, Ecological Entomology)

   Abstract Intensity and severity of bushfires in Australia have increased over the past few decades due to climate change, threatening habitat loss for numerous species. Although the impact of bushfires on vertebrates is well‐documented, the corresponding effects on insect taxa are rarely examined, although they are responsible for key ecosystem functions and services. Understanding the effects of bushfire seasons on insect distributions could elucidate long‐term impacts and patterns of ecosystem recovery.Here, the authors investigated the effects of recent bushfires, land‐cover change, and climatic variables on the distribution of a common and endemic dragonfly, the swamp tigertail (Synthemis eustalacta) (Burmeister, 1839), which inhabits forests that have recently undergone severe burning. The authors used a temporally dynamic species distribution modelling approach that incorporated 20 years of community‐science data on dragonfly occurrence and predictors based on fire, land cover, and climate to make yearly predictions of suitability. The authors also compared this to an approach that combines multiple temporally static models that use annual data.The authors found that for both approaches, fire‐specific variables had negligible importance for the models, while the percentage of tree and non‐vegetative cover were most important. The authors also found that the dynamic model outperformed the static ones, based on cross‐validation omission rate. Model predictions indicated temporal variation in area and spatial arrangement of suitable habitat, but no patterns of habitat expansion, contraction, or shifting.These results highlight not only the efficacy of dynamic modelling to capture spatiotemporal variables such as vegetation cover for an endemic insect species, but also provide a novel approach to mapping species distributions with sparse locality records. 

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